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// 3D 视觉基础（v12.9.0）
// 深度图处理、点云可视化、立体视觉基础
///|
/// 点云数据
pub(all) struct PointCloud {
points : Array[(Double, Double, Double)] // (x, y, z)
colors : Array[(Int, Int, Int)] // (r, g, b)
num_points : Int
}
///|
/// 创建空点云
pub fn point_cloud_new() -> PointCloud {
PointCloud::{ points: [], colors: [], num_points: 0 }
}
///|
/// 深度图可视化（伪彩色映射）
pub fn depth_to_colormap(
depth : @types.Image,
colormap? : String = "jet",
) -> @types.Image {
let cmap = colormap
let w = depth.width
let h = depth.height
let channels = depth.channels
let out_data : Array[Byte] = Array::make(w * h * 3, b'\x00')
// 找到深度范围
let mut min_depth = 10000000000.0
let mut max_depth = -10000000000.0
for y in 0..<h {
for x in 0..<w {
let idx = (y * w + x) * channels
let v = depth.data[idx].to_double()
if v < min_depth {
min_depth = v
}
if v > max_depth {
max_depth = v
}
}
}
let range = if max_depth > min_depth { max_depth - min_depth } else { 1.0 }
// 伪彩色映射
for y in 0..<h {
for x in 0..<w {
let idx = (y * w + x) * channels
let v = depth.data[idx].to_double()
let normalized = (v - min_depth) / range
let out_idx = (y * w + x) * 3
// Jet 颜色映射（蓝 -> 青 -> 绿 -> 黄 -> 红）
let (r, g, b) = if cmap == "jet" {
if normalized < 0.25 {
(0, (normalized * 4.0 * 255.0).to_int(), 255)
} else if normalized < 0.5 {
(0, 255, ((0.5 - normalized) * 4.0 * 255.0).to_int())
} else if normalized < 0.75 {
(((normalized - 0.5) * 4.0 * 255.0).to_int(), 255, 0)
} else {
(255, ((1.0 - normalized) * 4.0 * 255.0).to_int(), 0)
}
} else {
// 灰度映射
let gray = (normalized * 255.0).to_int()
(gray, gray, gray)
}
out_data[out_idx] = r.min(255).max(0).to_byte()
out_data[out_idx + 1] = g.min(255).max(0).to_byte()
out_data[out_idx + 2] = b.min(255).max(0).to_byte()
}
}
@types.Image::{
width: w,
height: h,
channels: 3,
data: Bytes::from_array(out_data),
}
}
///|
/// 深度图转点云
pub fn depth_to_pointcloud(
depth : @types.Image,
fx? : Double = 500.0,
fy? : Double = 500.0,
cx? : Double = 0.0,
cy? : Double = 0.0,
scale? : Double = 1.0,
) -> PointCloud {
let focal_x = fx
let focal_y = fy
let center_x = cx
let center_y = cy
let depth_scale = scale
let w = depth.width
let h = depth.height
let channels = depth.channels
let points : Array[(Double, Double, Double)] = []
let colors : Array[(Int, Int, Int)] = []
for y in 0..<h {
for x in 0..<w {
let idx = (y * w + x) * channels
let z = depth.data[idx].to_double() * depth_scale
if z > 0.0 {
let px = (x.to_double() - center_x) * z / focal_x
let py = (y.to_double() - center_y) * z / focal_y
points.push((px, py, z))
// 颜色基于深度
let normalized = z.min(255.0) / 255.0
let r = (normalized * 255.0).to_int()
let g = ((1.0 - normalized.abs() - 0.5) * 255.0).to_int()
let b = ((1.0 - normalized) * 255.0).to_int()
colors.push((r.min(255).max(0), g.min(255).max(0), b.min(255).max(0)))
}
}
}
PointCloud::{ points, colors, num_points: points.length() }
}
///|
/// 点云体素下采样（简化版）
pub fn pointcloud_voxel_downsample(
cloud : PointCloud,
voxel_size? : Double = 1.0,
) -> PointCloud {
let vs = voxel_size
// 简单的体素下采样：每个体素保留第一个点
let seen : Array[String] = []
let points : Array[(Double, Double, Double)] = []
let colors : Array[(Int, Int, Int)] = []
for i in 0..<cloud.points.length() {
let (x, y, z) = cloud.points[i]
let vx = (x / vs).floor().to_int()
let vy = (y / vs).floor().to_int()
let vz = (z / vs).floor().to_int()
let key = vx.to_string() + "_" + vy.to_string() + "_" + vz.to_string()
let mut found = false
for k in seen {
if k == key {
found = true
}
}
if !found {
seen.push(key)
points.push((x, y, z))
if i < cloud.colors.length() {
colors.push(cloud.colors[i])
} else {
colors.push((255, 255, 255))
}
}
}
PointCloud::{ points, colors, num_points: points.length() }
}
///|
/// 立体视觉：计算视差图（简化版，基于块匹配）
pub fn compute_disparity(
left : @types.Image,
right : @types.Image,
max_disparity? : Int = 64,
block_size? : Int = 7,
) -> @types.Image {
let max_disp = max_disparity
let bs = block_size
if left.width != right.width || left.height != right.height {
return left
}
let w = left.width
let h = left.height
let channels = left.channels.min(right.channels)
let half = bs / 2
let disparity : Array[Int] = Array::make(w * h, 0)
for y in half..<(h - half) {
for x in half..<(w - half) {
let mut best_disp = 0
let mut best_cost = 10000000000.0
for d in 0..<max_disp {
if x - d < half {
continue
}
// 计算 SAD（绝对差之和）
let mut cost = 0.0
for dy in -half..<(half + 1) {
for dx in -half..<(half + 1) {
let l_idx = ((y + dy) * w + (x + dx)) * channels
let r_idx = ((y + dy) * w + (x + dx - d)) * channels
for c in 0..<channels {
let diff = (left.data[l_idx + c].to_double() -
right.data[r_idx + c].to_double()).abs()
cost += diff
}
}
}
if cost < best_cost {
best_cost = cost
best_disp = d
}
}
disparity[y * w + x] = best_disp
}
}
// 转换为 8 位深度图
let out_data : Array[Byte] = Array::make(w * h, b'\x00')
for i in 0..<(w * h) {
let value = (disparity[i].to_double() / max_disp.to_double() * 255.0).to_int()
out_data[i] = value.min(255).max(0).to_byte()
}
@types.Image::{
width: w,
height: h,
channels: 1,
data: Bytes::from_array(out_data),
}
}
///|
/// 视差图转深度图
pub fn disparity_to_depth(
disparity : @types.Image,
baseline? : Double = 1.0,
focal_length? : Double = 500.0,
) -> @types.Image {
let b = baseline
let f = focal_length
let w = disparity.width
let h = disparity.height
let out_data : Array[Byte] = Array::make(w * h, b'\x00')
for y in 0..<h {
for x in 0..<w {
let idx = y * w + x
let d = disparity.data[idx].to_double()
if d > 0.0 {
let depth = (b * f / d).min(255.0).max(0.0)
out_data[idx] = depth.to_int().to_byte()
} else {
out_data[idx] = b'\x00'
}
}
}
@types.Image::{
width: w,
height: h,
channels: 1,
data: Bytes::from_array(out_data),
}
}